Early detection of cervical cancer using microarray analysis and gene regulatory rules

摘要

The near future in healthcare is the personalized medicine. It is mainly based on the analysis of an individual's genomic biomarkers and their regulation. The origin of many diseases can be revealed by knowing the gene regulation. To achieve this goal analysis of microarray data is essential which helps to collect genomic data and regulation details. This paper advised the analysis of Affymetrix micro array data of cervical cancer that helps in the early detection of cancer. At the end of this analysis phase, differentially expressed genes in cancer samples are identified and perform steps for finding regulation. Through the cellular signaling networks, genes regulate the expression of other genes, which finally results in stable phenotype structures such as tumor or non-tumor cells. Often, tumor and non-tumor cellular network contains some identical cancer-causing genes, but due to the corresponding gene regulatory network (GRN) in tumor networks, they eventually end up in forming the cancerous cells, whereas in non-tumor networks, they do not. The potential for cancer could be detected before it actually happens if we could estimate the basic gene regulatory function rules. This paper applies a mathematical approach to determine such regulatory rules for a set of cells containing cancer causing genes. The approach conferred here is based on the utilization of probabilistic Boolean network model on the selected genes.